Law / Frameworks / Crosswalk / OWASP LLM Top 10 to ETSI EN 304 223

The OWASP Top 10 for LLM Applications, mapped to the ETSI EN 304 223, Baseline Cyber Security Requirements for AI Models and Systems

5 of OWASP LLM Top 10's controls connect to 15 of ETSI EN 304 223's through 224 laws in 125 jurisdictions. Two controls connect when the same duties, in at least 2 laws we track, reach both. The connections are our own reading of the law; no framework publisher has reviewed or endorsed them.

From
OWASP Top 10 for LLM Applications, 2026 edition, August 2026, OWASP GenAI Security Project
To
ETSI EN 304 223, Baseline Cyber Security Requirements for AI Models and Systems, version 2.1.1, December 2025, European Telecommunications Standards Institute (ETSI)
See also
ETSI EN 304 223 to OWASP LLM Top 10, the other way · every pair in the crosswalk · law library as of

Download this mapping, with every law behind each connection: owasp-llm-top-10--etsi-en-304-223.csv · owasp-llm-top-10--etsi-en-304-223.json

OWASP LLM Top 10ETSI EN 304 223LawsJurisdictionsWhere the laws stand
LLM01:2026Prompt Injection 5.1.2-2System built to hold up against adversarial attacks, unexpected inputs and failures. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM01:2026 and 5.1.2-2
5.1.3-1Threat modelling and risk management, reviewed regularly and covering AI-specific attacks. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM01:2026 and 5.1.3-1
5.2.1-4Sensitive data such as training and test sets protected from unauthorised access. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM01:2026 and 5.2.1-4
5.4.2-2Logs analysed for anomalies, breaches and effects of drift or poisoning. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM01:2026 and 5.4.2-2
LLM02:2026Sensitive Information Disclosure 5.1.2-2System built to hold up against adversarial attacks, unexpected inputs and failures. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM02:2026 and 5.1.2-2
5.1.3-1Threat modelling and risk management, reviewed regularly and covering AI-specific attacks. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM02:2026 and 5.1.3-1
5.2.1-4Sensitive data such as training and test sets protected from unauthorised access. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM02:2026 and 5.2.1-4
5.4.2-2Logs analysed for anomalies, breaches and effects of drift or poisoning. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM02:2026 and 5.4.2-2
LLM03:2026Excessive Agency 5.1.2-6Access the AI system gets to other systems and data sources is minimal and risk assessed. 145 107 142 in force, 3 not yet in force
The 145 laws behind LLM03:2026 and 5.1.2-6

and 135 more, every one in the free file.

5.1.4-1Capability for human oversight designed in and maintained. 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind LLM03:2026 and 5.1.4-1

and 68 more, every one in the free file.

5.2.1-1Full inventory of assets, including their dependencies and connections. 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind LLM03:2026 and 5.2.1-1

and 68 more, every one in the free file.

5.2.4-1Developers keep an audit trail of system design and post-deployment maintenance plans... 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind LLM03:2026 and 5.2.4-1

and 68 more, every one in the free file.

5.4.2-1Operators log system and user actions. 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind LLM03:2026 and 5.4.2-1

and 68 more, every one in the free file.

5.2.2-5AI-specific incident management and recovery plans that are created, tested and maintained. 12 9 9 in force, 2 not yet in force, 1 proposed
The 12 laws behind LLM03:2026 and 5.2.2-5

and 2 more, every one in the free file.

5.1.4-3Where oversight is a risk control, the technical measures behind it are built,... 4 2 2 in force, 2 not yet in force
The 4 laws behind LLM03:2026 and 5.1.4-3
5.2.4-2.1Minimum record for training data: its source, such as a page URL, and when it was collected. 3 3 3 in force
The 3 laws behind LLM03:2026 and 5.2.4-2.1
5.1.3-2Threats developers cannot resolve are passed to operators and on to end-users. 2 2 2 in force
The 2 laws behind LLM03:2026 and 5.1.3-2
5.3.1-1Users told where and how their data is used, accessed and stored, with developers... 2 2 2 in force
The 2 laws behind LLM03:2026 and 5.3.1-1
5.3.1-2.1That guidance covers proper use, limits and likely failure modes. 2 2 2 in force
The 2 laws behind LLM03:2026 and 5.3.1-2.1
5.1.3-1Threat modelling and risk management, reviewed regularly and covering AI-specific attacks. 2 1 2 in force
The 2 laws behind LLM03:2026 and 5.1.3-1
LLM04:2026Supply Chain 5.1.2-2System built to hold up against adversarial attacks, unexpected inputs and failures. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM04:2026 and 5.1.2-2
5.1.3-1Threat modelling and risk management, reviewed regularly and covering AI-specific attacks. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM04:2026 and 5.1.3-1
5.2.1-4Sensitive data such as training and test sets protected from unauthorised access. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM04:2026 and 5.2.1-4
5.4.2-2Logs analysed for anomalies, breaches and effects of drift or poisoning. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM04:2026 and 5.4.2-2
LLM05:2026Data and Model Poisoning 5.1.2-2System built to hold up against adversarial attacks, unexpected inputs and failures. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM05:2026 and 5.1.2-2
5.1.3-1Threat modelling and risk management, reviewed regularly and covering AI-specific attacks. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM05:2026 and 5.1.3-1
5.2.1-4Sensitive data such as training and test sets protected from unauthorised access. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM05:2026 and 5.2.1-4
5.4.2-2Logs analysed for anomalies, breaches and effects of drift or poisoning. 3 2 2 in force, 1 not yet in force
The 3 laws behind LLM05:2026 and 5.4.2-2
Not connected to the ETSI EN 304 223: 5 of OWASP LLM Top 10's controls

No law we track reaches these

No duty in the laws we track reaches these controls.

How these connections are drawn

Each requirement line in the laws we track is read against the framework for its subject (the AI, scraping and news aggregation laws against the NIST AI RMF, the privacy, communications and age assurance laws against the NIST Privacy Framework, the cybersecurity laws against the NIST CSF 2.0); laws on liability and content moderation are not read against any framework yet. A line reaches either framework through a kind of duty its law carries, and only where that kind of duty also reaches, on that same line, the NIST framework for the line's own subject. Two controls connect when lines in at least 2 laws reach both. That says the same duties reach both; it does not say the two are alike, or that running either one does what a law asks.

Every duty line in the AI, scraping and news aggregation laws we track has been read against the framework for its subject.

Sources and licenses

OWASP GenAI Security Project, OWASP Top 10 for LLM Applications 2026, licensed CC BY-SA 4.0. Excerpted: only the first paragraph of each entry's Description is reproduced, verbatim.

Identifiers refer to ETSI EN 304 223 V2.1.1 (2025-12), © ETSI 2025, all rights reserved. ETSI owns the standard; no ETSI text (titles or sentences) is reproduced here. The standard is free to download from ETSI.